setwd("~/Dropbox/Perception_Inequality_wHannah/Survey Files/Harris 1981 Business Week U.S. Economy Survey, study no. 812027")
library(dplyr)
library(tidyr)
library(car)
# loading data
survey <- read.table("harris_s812027_spss.tab", header = TRUE)
# pid
survey$pid <- c(1:nrow(survey))
# study
survey$study <- 812027
# study year (year)
survey$year <- 1981
# geographic data (urban)
# survey$urban <- car::recode(survey$S13, "'Central City' = 'Urban'; 'Town' = 'Rural';
#                          'Suburb' = 'Suburban'; 'Rural' = 'Rural'")
survey$urban <- NA
# geographic data (region)
# survey$region <- survey$S11
# levels(survey$region) <- list(East=c("East_(1)", "East_(2)"), Midwest=c("Midwest_(5)", "Midwest_(6)"),
#                              South=c("South_(3)", "South_(4)"),
#                              West=c("West_(7)", "West_(8)"))
survey$region <- NA
# respondent head of household (hh)
# survey$hh <- as.factor(car::recode(survey$F9A, "c(1, 3) = 'Yes'; 2 = 'No'; 4 = 'Not sure'; else = NA"))
survey$hh <- NA
# increasing inequality (inequality)
survey$inequality <- as.factor(car::recode(survey$Q3C_2, "1 = 'Feel'; 2 = 'Don t feel'; 3 = 'Not sure'; else = NA"))
# inequality variable (inequality.variable)
survey$inequality.variable <- 1
# union (union.self)
survey$union.self <- dplyr::recode(survey$F3_1,
`1` = "Yes",
`0` = "No")
survey$union.self[survey$F3_4 == 1] <- "Not Sure"
survey$union.self <- factor(survey$union.self)
survey$union.other <- dplyr::recode(survey$F3_2,
`1` = "Yes",
`0` = "No")
survey$union.other[survey$F3_4 == 1] <- "Not Sure"
survey$union.other <- factor(survey$union.other)
# employment (employed)
# survey$employed <- survey$F10A
survey$employed <- NA
# occupation
survey$occupation <- factor(dplyr::recode(survey$F1,
`1` = "Professional",
`2` = "Manager, official",
`3` = "Proprietor (small business)",
`4` = "Clerical worker",
`5` = "Sales worker",
`6` = "Skilled craftsman, foreman",
`7` = "Operative, unskilled laborer (except farm)",
`8` = "Service worker",
`9` = "Farmer, farm manager, farm laborer",
`10` = "Student",
`11` = "Housewife",
`12` = "Military service",
`13` = "Unemployed",
`14` = "Retired",
`15` = "Welfare",
`16` = "Disabled",
`17` = "Other (SPECIFY)",
`18` = "Not sure/refused"))
summary(survey$occupation)
# household size (hhsize)
# survey$hhsize <- survey$F11A
survey$hhsize <- NA
# education (educ)
# survey$educ <- car::recode(survey$F4C, "c(1, 2, 3, 4) = 'Less than high school';
#                         5 = 'High school graduate';
#                        c(6, 7, 10) = 'Some college';
#                       8 = 'College graduate';
#                      9 = 'Post graduate';
#                     else = 'NA'")
# survey$educ <- factor(survey$educ, levels = c("Less than high school", "High school graduate", "Some college", "College graduate", "Post graduate"),
#                    labels = c("Less than high school", "High school graduate", "Some college", "College graduate", "Post graduate"),
#                   ordered = TRUE)
survey$educ <- NA
# household income (income)
survey$income <- factor(dplyr::recode(survey$F5,
`1` = "$7,500 or less",
`2` = "$7,501 to $15,000",
`3` = "$15,001 to $25,000",
`4` = "$25,001 to $35,000",
`5` = "$35,001 to $50,000",
`6` = "$50,001 or over",
`7` = "Not sure/no answer/refused"))
summary(survey$income)
# age
survey$age <- factor(dplyr::recode(survey$F2,
`1` = "18-20",
`2` = "21-24",
`3` = "25-29",
`4` = "30-34",
`5` = "35-39",
`6` = "40-49",
`7` = "50-64",
`8` = "65 and over",
`9` = "Refused"))
summary(survey$age)
# race
survey$race <- factor(dplyr::recode(survey$F6,
`1` = "White, but not Hispanic",
`2` = "Black, but not Hispanic",
`3` = "Spanish-American (Mexican, Cuban, Puerto Rican, Central or South American",
`4` = "Asian (Oriental) or Pacific Islander",
`5` = "American Indian or Alaskan native",
`6` = "Not sure"))
summary(survey$race)
# politics (party)
survey$party <- factor(dplyr::recode(survey$F4,
`1` = "Republican",
`2` = "Democrat",
`3` = "Independent",
`4` = "Other",
`5` = "Not sure"))
summary(survey$party)
# politics (ideology)
# survey$ideology <- survey$F9
survey$ideology <- NA
# gender
# survey$gender <- survey$F15
summary(survey$S1)
survey$gender <- factor(dplyr::recode(survey$S1,
`1` = "Male",
`2` = "Female"))
summary(survey$gender)
# religion
# survey$religion <- survey$F5
survey$religion <- NA
# subset
survey_812027 <- survey[ ,c("pid", "study", "year", "urban", "region", "hh",
"inequality", "inequality.variable", "union.self", "union.other",
"employed", "occupation", "hhsize", "educ", "income",
"age", "race", "party", "ideology", "gender", "religion")]
summary(survey_812027)
saveRDS(survey_812027, file = "survey_812027.rds")
